2018 RayADistributedFrameworkforEmer

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Subject Headings: Ray; Distributed Machine Learning System.

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Abstract

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray --- a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 RayADistributedFrameworkforEmerIon Stoica
Philipp Moritz
Robert Nishihara
Stephanie Wang
Alexey Tumanov
Richard Liaw
Eric Liang
Melih Elibol
Zongheng Yang
William Paul
Ray: A Distributed Framework for Emerging AI Applications